Cancer in female firefighters: The clinicobiological, psychological, and social perspectives.

Objectives: This study explored how demographic characteristics, life experiences, and firefighting exposures have an impact on cancer among female firefighters, and described the types and biologic characteristics of cancers as reported by women in the fire service. Methods: The online survey was available from June 2019 to July 2020. Questions related to demographic characteristics, lifestyle factors, firefighting exposures, and cancer diagnoses. Descriptive analyses characterized variables by the presence or absence of cancer. Qualitative data provided insight into both firefighting and cancer experiences among women. Results: There were 1,344 female firefighter respondents from 12 different countries, 256 of whom provided information on their cancer diagnosis. North American respondents made up 92% of the total. Those with cancer were older, had been in the fire service longer, had more career fires and toxic exposures, and were less likely to still be in active service. They also reported more tobacco use, and more full-term pregnancies. There were no differences in family history of cancer between the two groups. The average age at diagnosis was 39.0 years. The major types of cancer reported included breast (25.4%), cervical (21.1%), melanoma (20.7%), base cell/skin (16.4%), and uterine (14.8%). The cancer was detected when seeking medical attention for symptoms (42.1%), during routine health screening (29.8%), and during specific cancer screening (28.1%). The stage of cancer was reported by 44.5%, and 30.9% included the histopathological grade. Treatments included surgery (72.7%), chemotherapy (14.8%), radiotherapy (13.7%), and observation (13.7%). Challenges associated with cancer included psychosocial (33.2%), financial (18.8%), physical (6.6%), and spiritual (6.3%). Concerns about reporting a cancer experience to their employer included the desire to keep health information private (11.3%), a feeling of vulnerability (7.4%), and being perceived as weak (7.0%). Lack of support from their employer or insurer was also noted. Conclusion: Female firefighters experienced a wide variety of different types of cancers which may come earlier than similar cancers in the public. These findings can help inform resource allocation, the development of new policies, and the need for broader presumptive coverage to support female firefighters diagnosed with cancer.

The Mentor's Role in Fostering Research Integrity Standards Among New Generations of Researchers: A Review of Empirical Studies.

Promoting research integrity practices among doctoral candidates and early career researchers is important for creating a stable and healthy research environment. In addition to teaching specific technical skills and knowledge, research supervisors and mentors inevitably convey research practices, both directly and indirectly. We conducted a scoping review to summarise the role of mentors in fostering research integrity practices, mentors' responsibilities and the role that institutions have in supporting good mentorship. We searched five different databases and included studies that used an empirical methodology. After searching, a total of 1199 articles were retrieved, of which 24 were eligible for analysis. After snowballing, a total of 35 empirical articles were selected. The review discusses various themes such as the importance of good mentorship, poor mentorship practices, virtues and qualities of mentors, responsibilities and activities of mentors, group mentoring and responsibilities of the institution in supporting good mentorship. This review demonstrates the importance of mentors instilling responsible research practices and attitudes, and promoting research integrity among their mentees. Mentors are responsible for providing explicit guidance and for acting as good role models. The review highlights how poor mentorship can have a bad impact on the research climate. In addition, the review highlights the important influence that institutions can have in supporting mentorship.

A Technical Guide for Performing Spectroscopic Measurements on Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) offer a unique platform to understand light-driven processes in solid-state materials, given their high structural tunability. However, the progression of MOF-based photochemistry has been hindered by the difficulty in spectrally characterizing these materials. Given that MOFs are typically larger than 100 nm in size, they are prone to excessive light scatter, thereby rendering data from valuable analytical tools like transient absorption and emission spectroscopy nearly uninterpretable. To gain meaningful insights of MOF-based photo-chemical and physical processes, special consideration must be taken toward properly preparing MOFs for spectroscopic measurements, as well as the experimental setups that garner higher quality data. With these considerations in mind, the present guide provides a general approach and set of guidelines for the spectroscopic investigation of MOFs. The guide addresses the following key topics: (1) sample preparation methods, (2) spectroscopic techniques/measurements with MOFs, (3) experimental setups, (3) control experiments, and (4) post-run stability characterization. With appropriate sample preparation and experimental approaches, pioneering advancements toward the fundamental understanding of light-MOF interactions are significantly more attainable.

Variations in national surveillance reporting for Mpox virus: A comparative analysis in 32 countries.

Objectives: Case Reporting and Surveillance (CRS) are crucial to combat the global spread of the Monkeypox virus (Mpox). To support CRS, the World Health Organization (WHO) has released standardized case definitions for suspected, probable, confirmed, and discarded cases. However, these definitions are often subject to localized adaptations by countries leading to heterogeneity in the collected data. Herein, we compared the differences in Mpox case definitions in 32 countries that collectively reported 96% of the global Mpox caseload. Methods: We extracted information regarding Mpox case definitions issued by the competent authorities in 32 included countries for suspected, probable, confirmed, and discarded cases. All data were gathered from online public sources. Results: For confirmed cases, 18 countries (56%) followed WHO guidelines and tested for Mpox using species specific PCR and/or sequencing. For probable and suspected cases, seven and eight countries, respectively were found to have not released definitions in their national documentations. Furthermore, none of the countries completely matched WHO's criteria for probable and suspected cases. Overlapping amalgamations of the criteria were frequently noticed. Regarding discarded cases, only 13 countries (41%) reported definitions, with only two countries (6%) having definition consistent with WHO guidelines. For case reporting, 12 countries (38%) were found to report both probable and confirmed cases, in line with WHO requirements. Conclusion: The heterogeneity in case definitions and reporting highlights the pressing need for homogenization in implementation of these guidelines. Homogenization would drastically improve data quality and aid data-scientists, epidemiologists, and clinicians to better understand and model the true disease burden in the society, followed by formulation and implementation of targeted interventions to curb the virus spread.

Measuring stillbirth and perinatal mortality rates through household surveys: a population-based analysis using an integrated approach to data quality assessment and adjustment with 157 surveys from 53 countries.

BACKGROUND: In most low-income and middle-income countries (LMICs), national surveys are the main data source for stillbirths and perinatal mortality. Data quality issues such as under-reporting and misreporting have greatly limited the usefulness of such data. We aimed to enhance the use of mortality data in surveys by proposing data quality metrics and exploring adjustment procedures to obtain the best possible measure of perinatal mortality. METHODS: We performed a population-based analysis of data from 157 demographic and health surveys (DHSs) from 1990 to 2020, with reproductive calendar and birth history data from 53 LMICs. Pregnancies terminated before 7 months' gestation were excluded. We examined data quality and compared survey values with reference values obtained from a literature review to assess misreporting of the age at early neonatal death, omission and transference of stillbirths, and very early neonatal deaths. Real cohort life-table rates of stillbirth, early neonatal, and perinatal mortality per 1000 births were calculated. The underlying risks of stillbirth and daily deaths were modelled using modified Gompertz-Makeham models. FINDINGS: Data for 2 008 807 pregnancies of ≥7 months' gestational age were extracted from the reproductive calendar for the analysis period. Age heaping at day 7 occurred in most surveys. The median value for the heaping index of deaths at day 7 was 2·05 (IQR 1·36-2·87). The median ratio of stillbirths to deaths on days 0-1 was 1·15 (0·86-1·51). Of the 157 surveys, 23 (15%) were considered to have plausible ratios, 71 (45%) had probable ratios, and 63 (40%) had improbable ratios. The ratio of deaths on days 0-1 to deaths on days 2-6 varied considerably between surveys and 119 surveys (76%) had ratios of less than 2·4, indicative of under-reporting of very early neonatal deaths in most surveys. The fully adjusted model increased the median stillbirth rates from 12·2 (9·4-15·9) to 25·6 (18·0-33·4) per 1000 births, with a median relative increase of 95·0% (56·6-136·6). The median perinatal mortality rate also increased from 32·6 (23·6-38·3) to 44·8 (32·8-58·0) per 1000 births, with a median relative increase of 47·8% (6·9-61·0). INTERPRETATION: A simultaneous focus on stillbirths and early neonatal mortality facilitates a comprehensive assessment of inaccurate reporting in household surveys and allows for better use of surveys in planning and monitoring of efforts to reduce stillbirths and early neonatal mortality. FUNDING: None.

The Necessity of Multiple Data Sources for ECG-Based Machine Learning Models.

Even though the interest in machine learning studies is growing significantly, especially in medicine, the imbalance between study results and clinical relevance is more pronounced than ever. The reasons for this include data quality and interoperability issues. Hence, we aimed at examining site- and study-specific differences in publicly available standard electrocardiogram (ECG) datasets, which in theory should be interoperable by consistent 12-lead definition, sampling rate, and measurement duration. The focus lies upon the question of whether even slight study peculiarities can affect the stability of trained machine learning models. To this end, the performances of modern network architectures as well as unsupervised pattern detection algorithms are investigated across different datasets. Overall, this is intended to examine the generalization of machine learning results of single-site ECG studies.

Cross-Registry Benchmarking of Data Quality: Lessons Learned.

Feedback of data quality measures to study sites is an established procedure in the management of registries. Comparisons of data quality between registries as a whole are missing. We implemented a cross-registry benchmarking of data quality within the field of health services research for six projects. Five (2020) and six (2021) quality indicators were selected from a national recommendation. The calculation of the indicators was adjusted to the registries' specific settings. Nineteen (2020) and 29 results (2021) could be included in the yearly quality report. Seventy-four per cent (2020) and 79% (2021) of the results did not include the threshold in their 95%-confidence-limits. The benchmarking revealed several starting points for a weak-point analysis through a comparison of results with a predefined threshold as well as through comparisons among each other. In the future, a cross-registry benchmarking might be part of services provided through a health services research infrastructure.

Social Determinants of Health Data Quality at Different Levels of Geographic Detail.

Social determinants of health (SDOH) impact 80% of health outcomes from acute to chronic disorders, and attempts are underway to provide these data elements to clinicians. It is, however, difficult to collect SDOH data through (1) surveys, which provide inconsistent and incomplete data, or (2) aggregates at the neighborhood level. Data from these sources is not sufficiently accurate, complete, and up-to-date. To demonstrate this, we have compared the Area Deprivation Index (ADI) to purchased commercial consumer data at the individual-household level. The ADI is composed of income, education, employment, and housing quality information. Although this index does a good job of representing populations, it is not adequate to describe individuals, especially in a healthcare context. Aggregate measures are, by definition, not sufficiently granular to describe each individual within the population they represent and may result in biased or imprecise data when simply assigned to the individual. Moreover, this problem is generalizable to any community-level element, not just ADI, in so far as they are an aggregate of the individual community members.

The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials.

In medical research, the traditional way to collect data, i.e. browsing patient files, has been proven to induce bias, errors, human labor and costs. We propose a semi-automated system able to extract every type of data, including notes. The Smart Data Extractor pre-populates clinic research forms by following rules. We performed a cross-testing experiment to compare semi-automated to manual data collection. 20 target items had to be collected for 79 patients. The average time to complete one form was 6'81" for manual data collection and 3'22" with the Smart Data Extractor. There were also more mistakes during manual data collection (163 for the whole cohort) than with the Smart Data Extractor (46 for the whole cohort). We present an easy to use, understandable and agile solution to fill out clinical research forms. It reduces human effort and provides higher quality data, avoiding data re-entry and fatigue induced errors.

Identifying Relevant FHIR Elements for Data Quality Assessment in the German Core Data Set.

The German Medical Informatics Initiative makes clinical routine data available for biomedical research. In total, 37 university hospitals have set up so-called data integration centers to facilitate this data reuse. A standardized set of HL7 FHIR profiles ("MII Core Data Set") defines the common data model across all centers. Regular Projectathons ensure continuous evaluation of the implemented data sharing processes on artificial and real-world clinical use cases. In this context, FHIR continues to rise in popularity for exchanging patient care data. As reusing data from patient care in clinical research requires high trust in the data, data quality assessments are a key point of concern in the data sharing process. To support the setup of data quality assessments within data integration centers, we suggest a process for finding elements of interest from FHIR profiles. We focus on the specific data quality measures defined by Kahn et al.

Towards a Consistent Representation of Contradictions Within Health Data for Efficient Implementation of Data Quality Assessments.

Contradictions as a data quality indicator are typically understood as impossible combinations of values in interdependent data items. While the handling of a single dependency between two data items is well established, for more complex interdependencies, there is not yet a common notation or structured evaluation method established to our knowledge. For the definition of such contradictions, specific biomedical domain knowledge is required, while informatics domain knowledge is responsible for the efficient implementation in assessment tools. We propose a notation of contradiction patterns that reflects the provided and required information by the different domains. We consider three parameters (α, ß, θ): the number of interdependent items as α, the number of contradictory dependencies defined by domain experts as ß, and the minimal number of required Boolean rules to assess these contradictions as θ. Inspection of the contradiction patterns in existing R packages for data quality assessments shows that all six examined packages implement the (2,1,1) class. We investigate more complex contradiction patterns in the biobank and COVID-19 domains showing that the minimum number of Boolean rules might be significantly lower than the number of described contradictions. While there might be a different number of contradictions formulated by the domain experts, we are confident that such a notation and structured analysis of the contradiction patterns helps to handle the complexity of multidimensional interdependencies within health data sets. A structured classification of contradiction checks will allow scoping of different contradiction patterns across multiple domains and effectively support the implementation of a generalized contradiction assessment framework.

Data Quality Assessment for Observational Medical Outcomes Partnership Common Data Model of Multi-Center.

The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.

Local Data Quality Assessments on EHR-Based Real-World Data for Rare Diseases.

The project "Collaboration on Rare Diseases" CORD-MI connects various university hospitals in Germany to collect sufficient harmonized electronic health record (EHR) data for supporting clinical research in the field of rare diseases (RDs). However, the integration and transformation of heterogeneous data into an interoperable standard through Extract-Transform-Load (ETL) processes is a complex task that may influence the data quality (DQ). Local DQ assessments and control processes are needed to ensure and improve the quality of RD data. We therefore aim to investigate the impact of ETL processes on the quality of transformed RD data. Seven DQ indicators for three independent DQ dimensions were evaluated. The resulting reports show the correctness of calculated DQ metrics and detected DQ issues. Our study provides the first comparison results between the DQ of RD data before and after ETL processes. We found that ETL processes are challenging tasks that influence the quality of RD data. We have demonstrated that our methodology is useful and capable of evaluating the quality of real-world data stored in different formats and structures. Our methodology can therefore be used to improve the quality of RD documentation and to support clinical research.

Protocol of Validation Mechanisms Within the Design of an Electronic Case Report Form for the MULTI-SITA Project.

With the wide diffusion of web technology, dedicated electronic Case Report Forms (eCRFs) became the main tool for collecting patient data. The focus of this work is to thoroughly consider the data quality in every aspect of the design of the eCRF, with the result of having multiple steps of validation that should produce a diligent and multidisciplinary approach towards every step of data acquisition. This goal affects every aspect of the system design.

A Model for Cognitive Personalization of Microtask Design.

The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker's cognitive profile. There are two common methods for assessing a crowd worker's cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model's performance.

Growing beyond sickle cell disease: A metasynthesis of children, adolescents, and young adult experiences living with sickle cell disease.

Children, adolescents, and young adults living with sickle cell disease (SCD) often experience an unpredictable and complex disease course. Although there is a growing literature on the lived experience of patients with SCD, qualitative syntheses are lacking. Therefore, a qualitative metasynthesis was conducted to inform care and potential interventions. Noblit and Hare's phases of metaethnographic research were used to guide the synthesis of qualitative data. Data extracted from the identified studies were directly compared through reciprocal translation. The 12 studies that met inclusion criteria for the meta-synthesis included 177 participants ranging in age from 6 to 35 years old from six different countries. The authors identified three key metaphors: Ubiquitous Intrusion, Coping to Learn: Learning to Cope, and Part of the Whole. The metaphors were elucidated by three essential concepts that underlie the experience of children, adolescents, and young adults living with SCD: (1) recognition of SCD implications, (2) identifying ways to balance responsibilities, and (3) positioning oneself to thrive with SCD. The metaphors and essential concepts support the global theme of "Growing Beyond SCD." The metasynthesis revealed the shared complexity of living with SCD among children, adolescents, and young adults from diverse cultures in which the yearning for a normal life drove learning to adapt and manage SCD with their support network. The key metaphors may be used to guide development of nursing interventions designed to promote self-acceptance, coping, and adaptation skills among children, adolescents, and young adults that will help them to flourish while managing SCD as a chronic condition.

A Metapsychology of the Unrepresented.

The terms unrepresented and unrepresented states are increasingly being referred to in psychoanalytic discourse, without our having established a generally agreed upon consensus about their definition, use or meaning. While these particular designations were never used by Freud, a careful reading of his work reveals them to be qualities that characterize the initial state of both the drive and perception. This paper attempts to place these terms in a clinically useful, metapsychological perspective by reviewing their conceptual origin in Freud and examining their elaboration and clinical relevance in the work of Bion, Winnicott, and Green. These concepts should prove especially useful for understanding and addressing problems presented by non-neurotic patients and psychic organizations and will help expand the reach and efficacy of psychoanalytic understanding and technique to increasing numbers of contemporary patients.

Improving the quality of external cause of death data in Brazil: evaluation and validation of a new form to investigate garbage codes.

Garbage codes, such as external causes with no specific information, indicate poor quality cause of death data. Investigation of garbage codes via an effective instrument is necessary to convert them into useful data for public health. This study analyzed the performance and suitability of the new investigation of deaths from external causes (IDEC) form to improve the quality of external cause of death data in Brazil. The performance of the IDEC form on 133 external garbage codes deaths was compared with a stratified matched sample of 992 (16%) investigated deaths that used the standard garbage codes form. Consistency between these two groups was checked. The percentage of garbage codes from external causes reclassified into valid causes with a 95% confidence interval (95%CI) was analyzed. Reclassification for specific causes has been described. Qualitative data on the feasibility of the form were recorded by field investigators. Investigation using the new form reduced all external garbage codes by -92.5% (95%CI: -97.0; -88.0), whereas the existing form decreased garbage codes by -60.5% (95%CI: -63.5; -57.4). The IDEC form presented higher effectivity for external-cause garbage codes of determined intent. Deaths that remained garbage codes mainly lacked information about the circumstances of poisoning and/or vehicle accidents. Despite the fact that field investigators considered the IDEC form feasible, they suggested modifications for further improvement. The new form was more effective than the current standard form in improving the quality of defined external causes.